Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.
about
Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortalityMining electronic health records: towards better research applications and clinical careThe coming age of data-driven medicine: translational bioinformatics' next frontierTYK2 protein-coding variants protect against rheumatoid arthritis and autoimmunity, with no evidence of major pleiotropic effects on non-autoimmune complex traitsDeciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin ActionIdentifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF ProjectTrends in biomedical informatics: automated topic analysis of JAMIA articlesPrioritizing Approaches to Engage Community Members and Build Trust in Biobanks: A Survey of Attitudes and Opinions of Adults within Outpatient Practices at the University of MarylandExtracting research-quality phenotypes from electronic health records to support precision medicineSecondary use of clinical data: the Vanderbilt approachValidation and enhancement of a computable medication indication resource (MEDI) using a large practice-based datasetThe Electronic Medical Records and Genomics (eMERGE) Network: past, present, and futureDevelopment and evaluation of an ensemble resource linking medications to their indicationsApplying semantic web technologies for phenome-wide scan using an electronic health record linked BiobankMining the human phenome using semantic web technologies: a case study for Type 2 DiabetesIntegrating EMR-linked and in vivo functional genetic data to identify new genotype-phenotype associationsThe architecture of risk for type 2 diabetes: understanding Asia in the context of global findings.The wave has finally broken: now what?An evaluation of the NQF Quality Data Model for representing Electronic Health Record driven phenotyping algorithmsApplying active learning to high-throughput phenotyping algorithms for electronic health records dataCombining structured and unstructured data to identify a cohort of ICU patients who received dialysis.Novel and emerging strategies for longitudinal data collection.Variation in Cohorts Derived from EHR Data in Four Care Delivery Settings.Using EHR-Linked Biobank Data to Study Metformin Pharmacogenomics.Investigating the relationship between mitochondrial genetic variation and cardiovascular-related traits to develop a framework for mitochondrial phenome-wide association studiesEnvironment-wide association study (EWAS) for type 2 diabetes in the Marshfield Personalized Medicine Research Project BiobankDiscriminative and Distinct Phenotyping by Constrained Tensor Factorization.eMERGEing progress in genomics-the first seven years.PARAMO: a PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records.Phenome-Wide Association Studies as a Tool to Advance Precision Medicine.Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records.Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record.Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitusMultiple metabolic genetic risk scores and type 2 diabetes risk in three racial/ethnic groups.Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems.Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records.Epigenetics in sports.Design patterns for the development of electronic health record-driven phenotype extraction algorithms.The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects
P2860
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P2860
Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
2011年论文
@zh
2011年论文
@zh-cn
name
Use of diverse electronic medi ...... genome-wide association study.
@ast
Use of diverse electronic medi ...... genome-wide association study.
@en
type
label
Use of diverse electronic medi ...... genome-wide association study.
@ast
Use of diverse electronic medi ...... genome-wide association study.
@en
prefLabel
Use of diverse electronic medi ...... genome-wide association study.
@ast
Use of diverse electronic medi ...... genome-wide association study.
@en
P2093
P2860
P50
P1476
Use of diverse electronic medi ...... genome-wide association study.
@en
P2093
Aaron W Miller
Abel N Kho
Christopher G Chute
Christopher S Carlson
Cynthia L Leibson
David R Crosslin
Joshua C Denny
Katherine M Newton
Laura Rasmussen-Torvik
Loren L Armstrong
P2860
P304
P356
10.1136/AMIAJNL-2011-000439
P577
2011-11-19T00:00:00Z